Assessment of a lumped coupled flow‐isotope model in data scarce Boreal catchments
Bibliographic record
Abstract
Abstract Quantifying streamflow sources within remote, data scarce, Boreal catchments remains a significant challenge because of limited accessibility and complex, flat topography. The coupled use of hydrometric and isotopic data has previously been shown to facilitate quantification of streamflow sources, but application has generally been limited to small basins and short time scales. A lumped flow‐isotope model was used to estimate contributing streamflow sources (soil, ground, and wetland water) over a four‐year period in two large nested headwater catchments (Sapochi and Odei Rivers) in northern Manitoba, Canada. On average, the primary streamflow source was estimated as soil water (60%) in the Sapochi River, and groundwater (54%) in the Odei River. A strong seasonal influence was observed: soil water was the primary streamflow source in summer, changing to groundwater and wetlands during the winter. Interannual variability in streamflow sources was strongly linked to the presence or absence of late summer rainfall. The greatest uncertainties in source quantification were identified during the spring freshets and high precipitation events, and hence, simulations may be improved through explicit representation of the soil freeze/thaw process and data collection during this period. Assessment of primary streamflow components and qualitative uncertainty estimation using coupled isotope‐flow modelling is an effective method for first‐order identification of streamflow sources in data sparse remote headwaters. Copyright © 2016 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".